Understanding the Southeast Asian haze

Abstract

The Southeast Asian region had been subjected to a drastic reduction in air quality from the biomass burnings that occurred in 2013 and 2015. The smoke from the biomass burnings covered the entire region including Brunei, Indonesia, Malaysia, Singapore and Thailand, with haze particulate matter (PM) reducing the air quality to hazardous levels. Here we report a comprehensive size–composition–morphology characterization of the PM collected from an urban site in Singapore during the two haze events. The two haze events were a result of biomass burning and occurred in two different geographical source regions. We show the similarities and variations of particle size distribution during hazy and clear days during the two haze events. Sub-micron particles (<1 μm) dominate (~50%) the aerosols in the atmosphere during clear and hazy days. Using electron microscopy, we also categorize the PM, namely soot, organic–inorganic clusters and hybrid particles. The composition and morphology were similar in both the haze events. The majority of the PM is composed of carbon (~51%) and other elements pertaining to the earth's crust. The complexity of the mixing state of the PM is highlighted and the role of the capture mode is addressed. We also present the morphological characterization of all the classified PM. The box counting method is used to determine the fractal dimensions of the PM, and the dimensionality varied for every classification from 1.79 to 1.88. We also report the complexities of particles and inconsistencies in the existing approaches to understand them.

Full text

LETTER
Understanding the Southeast Asian haze
Karthik K R G
1,3
, T Baikie
1
, Mohan Dass E T
1
, Y Z Huang
2
and C Guet
1
1
Energy Research Institute @ NTU (ERI@N), 1 CleanTech Loop, #06-04, 637141, Singapore
2
School of Materials Science and Engineering, Nanyang Technological University, Block N4.1, Nanyang Avenue, 639798, Singapore
3
Author to whom any correspondence should be addressed.
E-mail: krgkarthik@ntu.edu.sg
Keywords: Southeast Asian haze, biomass burnings, particulate matter, electron microscopy, fractal dimensions
Supplementary material for this article is available online
Abstract
The Southeast Asian region had been subjected to a drastic reduction in air quality from the
biomass burnings that occurred in 2013 and 2015. The smoke from the biomass burnings
covered the entire region including Brunei, Indonesia, Malaysia, Singapore and Thailand, with
haze particulate matter (PM) reducing the air quality to hazardous levels. Here we report a
comprehensive size–composition–morphology characterization of the PM collected from an
urban site in Singapore during the two haze events. The two haze events were a result of biomass
burning and occurred in two different geographical source regions. We show the similarities and
variations of particle size distribution during hazy and clear days during the two haze events.
Sub-micron particles (<1mm) dominate (∼50%) the aerosols in the atmosphere during clear and
hazy days. Using electron microscopy, we also categorize the PM, namely soot, organic–inorganic
clusters and hybrid particles. The composition and morphology were similar in both the haze
events. The majority of the PM is composed of carbon (∼51%) and other elements pertaining to
the earth’s crust. The complexity of the mixing state of the PM is highlighted and the role of the
capture mode is addressed. We also present the morphological characterization of all the
classified PM. The box counting method is used to determine the fractal dimensions of the PM,
and the dimensionality varied for every classification from 1.79 to 1.88. We also report the
complexities of particles and inconsistencies in the existing approaches to understand them.
Introduction
Over the last two years the Southeast Asian region has
been a victim of one of the worst transboundary haze
episodes resultingfrom the burning of biomass material
(Ho et al 2014, Gaveau et al 2014, Koplitz et al 2016,Lee
et al 2017). Many of the burning events are started by
farmers who use slash-and-burn techniques in their
agricultural practices to clear the land (Lee et al 2017).
This increasedthe pollutant concentrations and resulted
in a drastic reduction in air quality. This not only
crippled human activity but also threatened the
electrical power generating systems that required a
continuous supply of clean air. It has also led to a decline
in the productivity of the affected regions. The biomass
burnings also produce soot with a radiative heating of
+0.5 to +0.6 W m
−2
(Jacobson 2001), making it the
second biggest contributor to global warming after
carbon dioxide (Jacobson 2001, Wentzel et al 2003).
The particulate matter (PM) or aerosols from these
burnings encompass a variety of conditions unique to
this particular region. Singapore is surrounded by the
archipelago of Sumatra on the west and Kalimantan on
the east, which are home to dense peat forests. The
haze events of 2013 and 2015 were a result of peat
forest burning in Sumatra and Kalimantan, respec-
tively. In addition to the geographical location, the
highly urbanized environment of Singapore also has
an impact on air quality. Together, the set of conditions
present a very unique and complex character to the
aerosols. They have been assumed to involve a
complex mixing state with characteristics that are
strongly dependant on geography (China et al 2013).
The smoke from biomass burning events travelled
across land and water masses before it was captured in
the urbanized environment of Singapore.
Recent studies on the physical and psychological
effects of haze suggested acute physical symptoms
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©2017 IOP Publishing Ltd

and mild psychological distress (Ho et al 2014,
Reid et al 2016), while the impact of exposure to
smoke was studied using a novel transport model and
global fire assimilation systems (GFAS) on Indonesia,
Malaysia and Singapore (Koplitz et al 2016).
Atmospheric emission studies performed using
remote sensing suggested that the emissions of the
2013 haze event represented 5%–10% of Indonesia’s
annual mean greenhouse gas (GHG) emissions
(Gaveau et al 2014). The problem of understanding
aerosol transport in the regions of Indonesia, Malaysia,
Thailand and Singapore was evaluated using weather
research and forecasting (WRF) models which
highlighted the spatiotemporal coverage and influence
of aerosols from biomass burning in the region (Lee
et al 2017). Throughout the literature the composition
and morphology was assumed to be known, but actual
data on chemistry and morphology was simply non-
existent as the literature did not refer to any event
pertaining to this region. In this spirit, a study to
understand the chemical and morphological proper-
ties of the PM has been carried out. We hope that by
determining the chemical nature, morphology and
size distribution of the PM we may be able to design
techniques for their mitigation from ambient air. In
addition, the findings described in this work could be
useful for elaborating the direct–indirect effects of the
PM on climate models in this region.
The present manuscript deals with a study of the
size–composition–morphology of the PM. We first
present the particle size distribution of the PM from the
haze events of 2013 and 2015, followed by a discussion
on their compaction and mixing state. This is followed
by electron microscopy analysis of the PM: a detailed
classification is given of the various types of PM
observed in this region. The observed links between the
composition and morphology is also outlined. Then we
detail the morphology of each form of PM. The notion
of using fractals, and its drawbacks in elucidating the
characteristics of the PM, is also discussed.
Results and discussion
Particle size distribution
The particle size distribution is derived while the air is
sampled in an aerosol monitor (TSI 8533). The
measuring system combines photometric measure-
ment of the particle cluster and optical sizing of single
particles. The monitors estimate size-segregated
aerosol mass concentration over a wide concentration
range (0.001–150 mg m
−3
) in real time. An analysis of
the particle size distribution of the PM was performed
on the data collected during the haze events of 2013
and 2015 and the clear days of 2015. The PM was then
classified into four bin sizes: <1mm, 1–2.5 mm,
2.5–4mm and 4–10 mm. The trend for the particle size
distribution for clear and hazy days is presented in
figure 1. As can be seen, smaller particles (<1mm)
dominated during the clear days and hazy days. This
has a large impact on general public health, as particles
of size PM
2.5
are the leading cause of pollution-related
mortality (Koplitz et al 2016). The clear days of April–
May were generally in the dry part of the year and
surrounding urbanization is a probable reason for the
sustained increase in sub-micron particles in the
atmosphere. There were similarities in this trend for all
bin sizes, with a few exceptions. The PM 2.5–4mm bin
increased almost four times in the 2013 haze event
compared with the 2015 haze event and three times
compared with the clear days in 2015. The PM 1–2.5
bin was found to be 16.1% in the 2013 haze, 31.5% in
the 2015 haze and 18.1% in the 2015 clear period.
The clear days of 2015 presented a similar particle
size distribution to the hazy days of 2015, implying the
existence of such a distribution even when there are
low concentrations of PM (<50 mgm
−3
) in the
atmosphere from a variety of sources within
Singapore. The concentration of PM was always
<50 mgm
−3
during the clear days and ∼300 mgm
−3
during both haze events. During the haze events, the
differences in the particle size distribution among the
three events could arise primarily due to the fact that
the hazes that reached Singapore in 2013 and 2015
were from different sources (Ho et al 2014, Gaveau
et al 2014, Koplitz et al 2016, Lee et al 2017). While the
2015 haze was due to burning in the Kalimantan
region (about 800 km from Singapore), the 2013
haze occurred due to burning in Sumatra (about
200 km from Singapore). Our observations also
support the concept that smaller particles tend to
travel further in the wind from their source region
Sept 2015
April 2015
June 2013
54
36
18
0
54
36
18
0
54
36
18
0
Percentage %Percentage %Percentage %
Size (µm)
50.1
31.5
7.5
10.9
56.9
8.5
18.1
16.5
43.7
29.4
10.8
16.1
12 4 1020
Hazy days
Hazy days
Clear days
200 - 300µg/m3
< 50 µg/m3
~300µg/m3
Figure 1. Size distribution of particulate matter collected
during the haze events of June 2013 and September 2015 and
clear days of April 2015. PM
1
is inclusive of the shaded region.
Environ. Res. Lett. 12 (2017) 084018
2

(Wilson and Suh 1997) (see figure S1 in the online
supplementary information available at stacks.iop.org/
ERL/12/084018/mmedia) However, the occurrence of
larger-sized particles was more related to the
agglomeration of smaller particles around the
sampling site rather than to the travel of primary
particles alone.
The sampling of particles in commercial aerosol
monitoring systems is performed under a set of valid
assumptions: (1) they assume that the particle is
spherical in nature and (2) they assume a value of
standard density/packing to calculate the physical
parameters such as concentration. This makes the
instruments prone to over/underestimation of aerosol
in the air sample. However, actual aerosol densities are
not uniform, the shape of aerosol particles is far from
spherical and their formation is a heterogeneous
process, with the surrounding environment being one
of the major influencing factors. This implies that the
true density of an aerosol would be very different from
the assumed value. For observations of soot particles,
reports indicate that the effective density of soot
particles varied from ∼1.2 g cm
−3
for 30 nm soot
particles to <0.3 g cm
−3
for 300 nm particles (Maricq
and Xu 2004), values which are much lower than the
density of carbon (∼2gcm
−3
) that the existing aerosol
measuring instruments have used to estimate aerosol
quantities in air. Manufacturers of aerosol measuring
instruments should reconsider the currently used
calibration standards because accurate information on
density and agglomeration factors would help detect
and understand aerosols more precisely.
Compaction of PM
For statistical analysis of high-yield sampling, filters
were used to capture the PM. The PM for the 2013
haze samples was collected from the air filters
positioned in front of gas turbines of power-generating
system in Singapore. The air filtration units in front of
the gas turbines comprise a two-stage process, with
coarse filters (G4 filter class, >10 mm) as the first stage
and fine filters (F9 filter class, >1mm) as the second.
One such filter set was used for the analysis of PM. The
PM collected from the filters was subjected to
compaction, which occurred mostly in the filter.
Due to particle compaction in the filter, information
regarding the history of formation, transport and
evolution of the PM was lost, hence it became difficult
to segregate the PM in pristine form and extract its
characteristics. Scanning electron microscope (SEM)
images of the state of compaction of the PM are
presented in figure 2. The samples were found to
be heavily coagulated and clumped together. The
sample sizes found in the coarse filter (figure 2(a))
were greater than 1 mm. Irregular morphology of the
PM was observed in all the samples. Distinct ‘bead-
shaped’structures (1–5mm in size) were also
identified in samples from the coarse filter (inset to
figure 2(a)). Figure 2(b) presents the PM from the fine
filters. Under a SEM, the PM from the fine filters did
not show any regular morphology. The coagulation of
PM in the fine filter was higher and more irregular
than that in the coarse filter. The inset of figure 2(b)
shows the PM on the fibers of the fine filter. Very high
charging was observed for all the PM samples under
the SEM, which required a coat of Pt as a conducting
layer on the PM samples. (China et al 2013) While the
coagulation features of PM from coarse and fine filters
differed significantly, the energy dispersive x-ray
spectra (EDS) presented a range of elemental
compositions which was similar in each case. The
EDS resulted in a majority of carbon (∼51%) and
other elements (O, Si, Na, Al, Cl, Ca) in both cases.
Classification and mixing state of PM
The mixing state of PM is known to be a subset of its
size–composition–morphology. However, due to
extreme sample compaction SEM could not provide
information on details of the aerosol state and
morphology. Hence, a more detailed analysis was
performed using a transmission electron microscope
(TEM). The characteristics of PM found in Singapore
were similar to those of PM found in other parts of the
world. Our samples bear a good correlation with
(a) (b)
Figure 2. Scanning electron microscope images of (a) particulate matter (PM) collected from the coarse filters (inset: coagulated lump
of a sample) and (b) PM collected from the fine filters (inset: PM coagulated on the fibers of the fine filter).
Environ. Res. Lett. 12 (2017) 084018
3

aerosol particles from biomass burning in South
Africa (Li 2003, Pósfai 2003), soot aggregates from
biomass burning and wildfires in California, India and
Mexico (Chakrabarty et al 2014) and tarballs from
biomass burning in Mexico (Adachi and Buseck
2011). Various forms of PM, such as soot, tar balls and
clusters, were observed in this region as well. Despite
the similarities, exceptions were also found to suggest a
complex growth model of PM in the region. Based on
morphology and elemental composition, the PM was
classified into three sets: soot particles (primary
particles), organic–inorganic clusters (tar balls and
other particles) and hybrid particles. While the
organic–inorganic clusters were found in the coarse
and fine filters, the soot and hybrid particles were only
found in the fine filters. One reason for this might be
due to the fact that the size of soot and hybrid particles
was much smaller than the pore size of the coarse
filters. Figure 3presents the TEM images of the
classified PM from the biomass burnings in 2013. An
EDS on the PM samples showed a very diverse
elemental composition. The TEM-EDS also reported
an elemental composition similar to that from the
SEM-EDS. Figure 4presents an example of the TEM-
EDS performed on the collected PM. Almost every
particle analysed was found to be carbonaceous. This
is evidence that haze events contributed to the
addition of carbon to the atmosphere, and over clear
days affected the climate in the region (Gaveau et al
2014, Lee et al 2017).
The structure of the soot particles has a typical
onion-shell type graphitic layer (figures 3(a) and (b)).
These particles were found in disordered clusters and
assumed to have been produced during biomass
burnings as primary particles with a near spherical
geometry (Maricq and Xu 2004). The soot was found
to be purely organic in nature as carbon showed up as
the only element in the TEM-EDS analysis (figure 4
(a)). The compact nature of the soot particles
indicated the formation of PM due to aging smoke.
Chain-like soot aggregates were presumed to be
formed in young smoke. They agglomerated further
due to aging and collapsed to form compact particles
(Li 2003). The observed soot was a loosely packed
conglomerate of many individual soot monomers.
The monomers were about 30–40 nm in diameter
while the aggregates had a maximum projected length
of about 300–500 nm. The aggregates’maximum
(a) (b)
(c) (d)
Figure 3. Transmission electron microscope images of the three different categories of the particulate matter found in the Southeast
Asian region: (a) soot cluster, (b) individual soot particle with ring-shaped graphitic layers, (c) organic–inorganic cluster with an inset
(scale bar 5 nm) of the inorganic islands in an organic matrix, (d) a hybrid particle.
Environ. Res. Lett. 12 (2017) 084018
4

projected length depended on the 3D packing of the
individual monomers.
The organic–inorganic cluster (figure 3(c)) con-
sisted of islands of inorganic material on a matrix of
carbon. This form was closer to the ‘organic with
inorganic inclusions’as observed in the South African
haze PM (Pósfai 2003). In most of our cases the
inorganic inclusions were not distinct and separate.
The inorganic islands were linked with carbon in a
complex manner. Carbon was found as a majority
component while traces of a variety of elements such
as O, Cu, Mg, Al, P, S, Fe, Na and Cl were also found.
With carbon as a majority component, the percentage
composition of the other elements varied from sample
to sample. A sample from this cluster is depicted in
figure 4(b). None of the samples found in these
clusters had a distinct geometry. Their shape, size and
elemental composition (organic–inorganic inclusion
ratio) varied from sample to sample. The average
maximum projected length of the organic–inorganic
clusters was about 200–600 nm. Found in large
numbers, they are understood to be formed due to
the condensation of inorganic vapors on the organic
phases. (China et al 2013, Pósfai 2003)
Hybrid particles presented in figure 3(d) are solid
particles with varying organic and inorganic content in
a complex ratio (figure 4(c)). These particles are
presumed to be a super-cooled form of giant organic–
inorganic clusters, making them larger than other
categories. These particles were opaque to the electron
beam and could sustain longer exposure times than
the soot particles. As they would have been subjected
to multiple nucleation–condensation growth–cooling
cycles they could be considered more aged than the
(a) (c)
(b)
C
Counts
4.8k
4.2k
3.6k
3.0k
2.4k
1.8k
1.2k
0.6k
0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 keV
Cu
O
Mg
Al S
P
Fe
Ni
Cu
0.80 1.60 2.40 3.20 4.00 4.80 5.60 6.40 7.20 8.00 keV
Distance (µm)
Counts
200
150
100
50
0123456
C
N
O
Si
S
K
Fe
Cu
Zn
Figure 4. The energy dispersive x-ray spectra (EDS) of the different categories of particulate matter found in the Southeast Asian
region: (a) soot, (b) an organic–inorganic cluster and (c) a line scan showing the distribution of elements along the hybrid particle.
Environ. Res. Lett. 12 (2017) 084018
5

other particle types. These particles also showed a
similar elemental composition to the organic–inor-
ganic cluster. Their elemental composition varied on
moving from one end of the particle to the other
(figure 4(c)). These particles were found with
diameters greater than 1 mm with near-geometrical
characteristics.
The coarse filter also contained particles similar to
the organic–inorganic cluster. The particles were larger
and had a similar elemental composition to the
organic–inorganic cluster. Their maximum projected
length was about 800–900 nm. Carbon was still a
majority element. Tar balls were also found attached
(figure 5) to these particles, showing that the PM
belonged to an intermediate stage of aging (Li 2003).
Tar balls varied significantly from the soot particles as
they did not possess any morphology.
While it was possible to predict the source of an
elemental constituent from the EDS results, under-
standing the nature of the mixing state is more
complex. Unlike PM found in other parts of the world,
much of this PM did not arrive in pristine form. This
implied that the mixing state of PM is disorderly and
dependent on the environmental factors surrounding
the source–transport until the point of capture. In
most of cases, the point of capture itself acts as an
obstacle and becomes the source of a unique mixing
state for the PM. In this case the air filtration units
ingested copious amounts of air, which would have
provided a more complex mixing state for the PM.
Hence, the mode of capture also plays a very
influential role in determining the nature of PM
formed.
Morphology of PM
The aerosols were usually found in irregular agglom-
erated forms. Their complex geometry enabled us to
understand their morphology using fractal-like anal-
ogies (Wentzel et al 2003, China et al 2013,
Chakrabarty et al 2014). Fractal analysis of the PM
brings addresses the morphology in terms of
geometric shape and volume. Mathematically, aerosols
are not classified as fractals; however, fractal analysis
techniques can still be used to understand their
morphologically based properties (Wentzel et al 2003,
China et al 2013, Wilson and Suh 1997, Chakrabarty
et al 2014, Schmidt-Ott 1988). The fractal dimension
(D
f
) is given by the statistical scaling law
N¼kg
2Rg
dp

Df
ð1Þ
where Nis the number of monomers per aggregate, R
g
is the radius of gyration of the aggregate, d
p
is the
monomer diameter and k
g
is the fractal prefactor
(Wentzel et al 2003, Brasil et al 1999, Adachi et al
2007). For a given D
f
,k
g
represents the level of
compactness of the aerosol cluster (Wentzel et al 2003,
China et al 2013, Brasil et al 1999, Adachi et al 2007).
D
f
and k
g
together provide a more complete view of
aerosol morphology (Wentzel et al 2003). Other
properties of aerosols include the total surface area of
the aggregate, which is a relevant property in materials
with an open structure, and the overlap coefficient
(C
ov
) of the monomers in the aggregate. The overlap
coefficient is defined as
Cov ¼dpdij
dp
 ð2Þ
where d
p
is the diameter of the monomer and d
ij
is the
distance between the two touching particles. Most of
the PM measurements today approximate the PM
to a solid sphere with a default D
f
=3. Such an
Na
Al
KNi
1.20 2.40 3.60 4.80 6.00 7.20 8.40 keV
Figure 5. Transmission electron microscope images of particulate matter from the coarse filter and the tar ball sample (inset) with the
energy dispersive x-ray spectrum of the sample.
Environ. Res. Lett. 12 (2017) 084018
6

approximation is far from reality in most cases. The
mixing state of PM has with complicated features, as
observed in the organic–inorganic clusters. Such
clusters do not possess a near perfect geometry and
have variable packing factors (D
f
and k
g
). The
reduction in D
f
will imply a reduction in the density
of the material. Hence, with a default D
f
the overall
amount of aerosol in the atmosphere is prone to
overestimation. Hence fractal analysis helps to address
such issues.
In our case, the d
p
for the soot monomers was
found to be 30–40 nm. It should be noted that
equations (1) and (2) were derived for soot-like
particles only (Wentzel et al 2003, China et al 2013,
Brasil et al 1999, Adachi et al 2007). In this scenario,
the aerosols are not only different from soot in
geometry but also in morphology and composition.
Equations (1) and (2) do not hold for cases that
include organic–inorganic clusters where monomers
are indistinguishable. Rather, the process in which the
clusters are formed would determine the method used
to analyse them. Hence, a more detailed treatment is
necessary in such cases where the distinction between
particle monomers and aggregates becomes indistin-
guishable. This also induces more complexity in
equation (1), where a more definitive approach is
needed to describe the relationship between Nand R
g
.
All these properties require a statistical approach with
many more data sets for any individual variety of
aerosol. Such case-specific treatments are beyond the
scope of this paper and it would not be possible to
explain all the aerosol varieties observed in figure 3.
Hence, there is a breakdown of the fractal analysis
techniques for cases beyond soot-like agglomerates.
To obtain the fractal dimensions from the TEM
images, a box counting method was used. The method
uses a binary system of foreground versus background.
The method determines the change of the foreground
in a fixed pixel frame. Principally, a TEM image is a
projection of the 3D sample on a 2D plane. The image
is divided into a number of square boxes of know size
(scaling factor). The variation of the desired detail of
the PM with its box size is noted and a logarithmic
regression plot of the box count against the box size is
plotted. The slope of the line gives the dimensionality
D
f
of the particle in the image
Df¼ logðcountÞ
logðbox sizeÞ:ð3Þ
The variation of slope for the classified PM is given
in figure 6. A certain degree of distortion in precision
accompanies this method as it does not account for
particle overlap, screening effects or cluster anisotropy
(Wentzel et al 2003). A summary of the fractal
properties is presented in table 1. The D
f
of all the PM
varied from 1.79 to 1.88, suggesting a pseudo-fractal
behavior. The fractal analysis resulted with a little
understanding of the PM. The analysis pointed out
drawbacks in the prevailing techniques for character-
izing all the categories of aerosols captured. An
understanding of their fractal-like behavior would
improve estimation of their physical properties, which
have a direct impact on their optical and thermody-
namic characteristics.
Conclusion
In summary, the particle size distribution of the
Southeast Asian haze was studied for hazy and clear
days of 2013 and 2015. Sub-micron PM (PM
1
and
below) dominated both hazy and clear days during
2013 and 2015. The trend of the bin-wise distribution
observed for both clear and hazy days was similar in
2015. The observed domination of PM
4
and below
compared with PM
10
and above is known to have a
serious impact on public health, especially on
14
12
10
8
6
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Log (Box Size)
Log (Counts)
Soot Monomer
Soot Cluster
Organic-inorganic cluster (200 - 600 nm)
Organic-inorganic cluster (800 - 900 nm)
Hybrid Particle
Figure 6. The variation of box size with the box counts for each of the particulate matter classifications found in the Southeast Asian
haze of 2013.
Environ. Res. Lett. 12 (2017) 084018
7

respiratory conditions. The uncertainties of the
measurement and analysis techniques were also
highlighted. Commercial sampling instruments come
with calibration standards that did not take the mixing
states into account. Fractal analysis was also
performed, emphasizing the drawbacks of such
analysis techniques for further understanding the
Southeast Asian region haze of 2013.
We analysed the composition and morphology of
the PM captured in industrial filters from the
Southeast Asian haze of 2013. Carbon was found to
be a majority component in all the particles. A
correlation between particle morphology, composi-
tion and size was observed. Three categories of PM
were identified, namely soot, organic–inorganic
clusters and hybrid particles. Each of the categories
varied in nucleation–growth transport–capture mech-
anisms, resulting in complex mixing states. The
mixing states in these categories were similar to those
observed from other geographical regions but also
contained unique aspects pertaining to this region.
During haze events these parameters deviate from the
norm and bring about uncertainties in the climate
models. The three mixing states identified help to
estimate their radiative forcing parameters for the
region with higher degrees of certainty. Hence the
composition–morphology–size information provided
in this study would be useful for climate modelling of
the Southeast Asian region.
Methods
PM sampling
Sampling for haze was performed at Tuas Power
generation Pte Ltd. This plant located amidst an
industrial hub in the west of Singapore and
contributes about 20% of Singapore’s total electrical
power supply. The particle size distribution was
collected by placing the aerosol monitor (TSI 8533) in
an environmental enclosure (figure S2 in the online
supplementary information). This enclosure was
placed in front of the air filtration units for the gas
turbines. The aerosol monitor enabled real-time
sampling of the air quality every second for a period
of one week for each event. For the 2013 haze event
data were recorded from 18 to 24 June; for the clear
days of 2015 data were recorded from 15 to 22 April;
and for the 2015 haze event data were recorded from
14 to 21 September. The tropical climate of Singapore
presented with the typical temperature and humidity
conditions during the clear days. The hazy days also
presented with typical temperatures for the time of
year together with low visibility.
Microscopy
The air filters in front of the gas turbines were used to
collect the samples of PM. The functioning of the
filters coincided with the haze event of June 2013. One
filter set consisting of a coarse (G4 class, >10 mm) and
fine (F9 class, >1mm) filter was used for the analysis
of PM. A JEOL JSM 7600 F instrument was used to
perform electron microscopy in scanning mode. The
PM from the coarse filters was collected by gently
tapping a small portion of the coarse filter on the
copper tape of the SEM holder. A thin film of PM was
deposited on the copper tape, which was used for
subsequent analysis. For the TEM analysis, a JEOL
2100 F microscope was used in a high-resolution
mode. A small piece of the fine filter was sonicated in
ethyl alcohol.
Fractal analysis
The fractal analysis was performed using the box
counting method in ImageJ Software. This software
uses a box counting method to estimate D
f
. The tool
systematically lays a series of grids of decreasing size
(the boxes) over an image and records the data (count)
for each successive box size. More information on the
software can be found at https://imagej.nih.gov/ij/
docs/guide/146-3.html.
Acknowledgments
This research work was financially supported by Energy
Market Authority EIRP project no. NRF2013EWT-
EIRP001-034. The authors gratefully acknowledge the
help of Tuas Power Generation Pte Ltd and Professor
Victor Wei-Chung Changfor support and cooperation.
Table 1. Morphological properties of the various types of particulate matter found in the Southeast Asian haze of 2013.
Maximum
projected length
Composition/type Morphology D
f
Comments
30–40 nm Individual soot particles Spherical shaped structures with ring-like layering
of carbon
1.8 Monomer
300–500 nm A cluster of soot particles A cluster of individual soot particles 1.79 Aggregate of
monomers
200–600 nm An cluster of organic (tar
balls)–inorganic particles
Tar balls in a matrix of amorphous carbon and
inorganic elements. No specific geometry
1.87 No monomers;
compacted cluster
800–900 nm A cluster of organic–inorganic
particles
A complex matrix of organic (amorphous carbon)
and inorganic elements
1.86 No monomers;
compacted cluster
3–5mmA hybrid particle A solid particle with near-polygonal geometry 1.88 Individual
compacted particle
Environ. Res. Lett. 12 (2017) 084018
8

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